class_docstr: An implementation of the Efficient Graph Convolution (EGC-S) layer of
  
  @misc{tailor2021adaptive,
    title={Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions},
    author={Shyam A. Tailor and Felix L. Opolka and Pietro Liò and Nicholas D. Lane},
    year={2021},
    eprint={2104.01481},
    note={GNNSys Workshop, MLSys '21 and HAET Workshop, ICLR '21}
  }
  
  Differences compared to the paper:
  * Allow using a single but different aggregator similar to EGC-M.
  * Different bases per edge-type
code: def input_state_dimension(self) -> int:
      return self.__input_state_dim
  
file_path: ptgnn/ptgnn/neuralmodels/gnn/messagepassing/egcmessagepassing.py
imports: import torch
  from torch import nn
  from typing import Dict, List, Tuple
  from ptgnn.neuralmodels.gnn.messagepassing.abstractmessagepassing import AbstractMessagePassingLayer
label_desc: Use image node_id 3 for calling the EGCMessagePassingLayer obj's underlying member method code with example usage: obj.input_state_dimension() and returns: self